Effect of Leptin Levels of Type 2 Diabetes Mellitus and Diabetic Complications
Bibliographic record
Abstract
PURPOSE: To investigate serum leptin levels in patients with type 2 diabetes mellitus (T2DM) and the relationship between leptin levels and T2DM complications and prevalence. METHODS: A total of 355 patients, 282 cases with T2DM and 73 normal controls, were recruited at 1st Medical Centre, Chinese PLA General Hospital (Beijing, China) between November 2013 and July 2014. Levels of serum leptin, biochemical markers and sexual hormones were measured, and clinical characteristics were retrieved through the electronic medical record system. RESULTS: Leptin levels in females were higher than that in males. Leptin levels in T2MD patients were positively correlated with body mass index, percent body fat, triglyceride, cystatin C homocysteine and salivary acid, and negatively correlated with glycosylated serum protein and glycosylated albumin levels. Leptin levels in males were positively correlated with systolic pressure and estradiol, and negatively correlated with testosterone and high density lipoprotein cholesterol. Sex (female) was positively correlated with the duration of disease. Leptin levels in T2DM patients with complications such as hypertension, diabetic nephropathy, diabetic peripheral neuropathy and coronary heart disease were higher than that in patients without such complications. Leptin levels in females with diabetic retinopathy and diabetic macroangiopathy were higher than that in patients without such complications, but there was no difference in males. CONCLUSIONS: Leptin has significant gender differences. Leptin levels are related to body mass index, percent body fat and sex hormone level in T2DM patients and may affect short-term blood glucose control in T2DM patients. Leptin levels are related to complications in patients with T2DM and affect the prevalence rates of complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".